A modified electromagnetism-like algorithm based on a pattern search method

The Electromagnetism-like (EM) algorithm, developed by Birbil and Fang [2] is a population-based stochastic global optimization algorithm that uses an attraction-repulsion mechanism to move sample points towards optimality. A typical EM algorithm for solving continuous bound constrained optimization problems performs a local search in order to gather information for a point, in the population. Here, we propose a new local search procedure based on the original pattern search method of Hooke and Jeeves, which is simple to implement and does not require any derivative information. The proposed method is applied to different test problems from the literature and compared with the original EM algorithm.(undefined

Build orientation optimization problem in additive manufacturing

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used by engineers and designers. Build orientation is a critical issue in AM since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduced significantly the building costs and will improve the object accuracy. This paper presents an optimization approach to solve the part build orientation problem considering the staircase effect, support area characteristics and the build time. Two global optimization methods, the Electromagnetism-like and the Stretched Simulated Annealing algorithms, are used to study the optimal orientation of four models. Preliminary experiments show that both optimization methods can effectively solve the build orientation problem in AM, finding several global solutions.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bézier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.This research is kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project #TIN2012-30768, Toho University, and the University of Cantabria. The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. We also thank the Editor and the two anonymous reviewers who helped us to improve our paper with several constructive comments and suggestions

A multi-objective approach to solve the build orientation problem in additive manufacturing

Additive manufacturing (AM) has been increasingly used in the creation of three-dimensional objects, layer-by-layer, from three-dimensional (3D) computer-aided design (CAD) models. The problem of determining the 3D model printing orientation can lead to reduced amount of supporting material, build time, costs associated with the deposited material, labor costs, and other factors. This problem has been formulated and studied as a single-objective optimization problem. More recently, due to the existence and relevance of considering multiple criteria, multi-objective approaches have been developed. In this paper, a multi-objective optimization approach is proposed to solve the part build orientation problem taking into account the support area characteristics and the build time. Therefore, the weighted Tchebycheff scalarization method embedded in the Electromagnetism-like Algorithm will be used to solve the part build orientation bi-objective problem of four 3D CAD models. The preliminary results seem promising when analyzing the Pareto fronts obtained for the 3D CAD models considered. Concluding, the multi-objective approach effectively solved the build orientation problem in AM, finding several compromise solutions.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019